How to read a book
...in the age of AI
I can’t predict the future of traditional book publishing and especially technical book publishing in the shadow of AI, nevertheless I can offer a few insights.
First, a sort of unrelated observation. In recent months and even years in some cases, I dialed back my presence on social media. Toot-a-loo Facebook and Twitter. Also an intentional slow down on Amazon Prime and the convenience of simply thinking of something and having it arrive within hours.
With the exception of a 3:30 a.m. Uber Eats delivery of Imodium to a NYC apartment — most things can wait.
I did try and announce I wasn’t going to renew Prime but the booing and hissing from the family was deafening — this will take some time.
Other small steps include walking to my local food-coop instead of driving to Whole Foods and the return of browsing in amazing bookstores — I recently found the best book for a panel talk I am moderating, just sitting there on the shelf — waiting for me to wander in.
Luddite traditions are also returning to my reliance on technology. I need to know what is going on under the hood. Even if I use QGIS for a project I always run it again with Whitebox Geospatial or in a Python notebook.
A well-stocked library is the same thing. The collection of books in the image above are from authors I have met in person at least briefly or in one case since they were born (myself).
Being an author isn’t for the meek so kudos to all.
“It is not the critic who counts; not the man who points out how the strong man stumbles, or where the doer of deeds could have done them better. The credit belongs to the man who is actually in the arena, whose face is marred by dust and sweat and blood; who strives valiantly; who errs, who comes short again and again, because there is no effort without error and shortcoming; but who does actually strive to do the deeds; who knows great enthusiasms, the great devotions; who spends himself in a worthy cause; who at the best knows in the end the triumph of high achievement, and who at the worst, if he fails, at least fails while daring greatly, so that his place shall never be with those cold and timid souls who neither know victory nor defeat.” — Theodore Roosevelt
I am of the mind if you don’t like the book — it wasn’t written for you. But I also think we might not know how we are supposed to use a book once we own it. In my case I heard “not enough Pandas” blah blah blah. Here is the rub. Books should teach you to think critically and solve your problems in real time. They are not recipes. If they aren’t serving you as a well-stocked pantry, you may end up very limited in what you can learn outside of a particular text.
In fact, I actually identified the target end-user for my Python book, Python for Geospatial Data Analysis…
Who Is This Book For?
My vision for Python for Geospatial Data Analysis presented me with a conundrum: how do you write a book for newly minted geospatial professionals who know Python and for newly minted Python programmers who are well versed in geospatial analytics? I decided simply to make it interesting. My goal isn’t to grant you professional expertise at either end of the spectrum but to bring us all together to learn tools and best practices. By the end of this book, I want all of you to feel proficient and confident enough to go out and explore geospatial analytics on your own. To that end, as I teach each tool and technique, I ask you to follow along, installing the tools as needed and using a Jupyter or Google Colab notebook to run code. But I don’t want you to stop there, so I also provide a host of different experiences that invite you to continue to explore
I hope there are insights for many. An important step in any new skill is foundational learning. Too many of us want to rush to the cool stuff. In my learning journey, I felt most books that were great for the tech made some pretty big assumptions on the variety of skillsets available to both the novice and intermediate learner.
Other books on my shelf aren’t read cover to cover although Hala’s book came close (note the crease worn into the binding). I use math to solidify calculations and estimations in geospatial and more so in the AI landscape.
Data engineering is another foundational core competency when you are working with data but in my use case — critical when working with teams. As a freelancer efficiency is king, second only to data quality and preparation.
Matt handed me his pocket reference when I met him at an O’Reilly table at the only Data Day Texas invitation I received. At first I liked it due to its size — little did I know how often I would tuck it in my computer bag as a reference, specifically for a project I had recently. When you don’t work in a specific role it might be months until you need to use Principal Component analysis (PCA) again — I use books for quick updates available in not only just-in-time scenarios but you can be pretty sure the resource is consistent — I am talking to you ChatGPT.
Books are my preferred way of using a reference. I am not a frequent user of AI tools but when my books were translated into other languages I used the opportunity to double check and refine code. For me personally I can’t imagine relying on provided code in the absence of foundational learning — code generated in AI should be cautionary — be careful what path you are on, you might get where you are headed.
Library
(Amazon associates get a few pennies if you buy through links)
Machine Learning Pocket Reference: Working with Structured Data in Python
Essential Math for AI: Next-Level Mathematics for Efficient and Successful AI Systems
Fundamentals of Data Engineering: Plan and Build Robust Data Systems